Settlements in the presence of leniency programs: Costs and benefits
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Over the last few decades, leniency programs have become important components of anti‐cartel policies in many jurisdictions. An extensive literature shows how such programs can destabilize cartels and even discourage their formation in the first place. Much less studied are settlement policies under which reduced fines are offered to settling parties late in the prosecution (when the probability of conviction is high). In particular, there has been little attention paid to the interaction of leniency and settlement policies. This paper examines whether the availability of late‐stage settlements could negatively impact the effectiveness of early‐stage leniency programs. Our main finding is that an appropriately designed settlement program can make collusion more difficult: In equilibrium, the adoption of an optimal settlement program by the Antitrust Authority reduces the occurrence of cartels by decreasing the long‐run gain from collusion. However, an overly generous settlement policy may undermine leniency programs and encourage the formation of more cartels.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it